Applications of Machine Learning in Chemical and Biological Oceanography.

Machine learning (ML) refers to computer algorithms that predict a meaningful output or categorize complex systems based on a large amount of data. ML is applied in various areas including natural science, engineering, space exploration, and even gaming development. This review focuses on the use of machine learning in the field of chemical and biological oceanography. In the prediction of global fixed nitrogen levels, partial carbon dioxide pressure, and other chemical properties, the application of ML is a promising tool. Machine learning is also utilized in the field of biological oceanography to detect planktonic forms from various images (i.e., microscopy, FlowCAM, and video recorders), spectrometers, and other signal processing techniques. Moreover, ML successfully classified the mammals using their acoustics, detecting endangered mammalian and fish species in a specific environment. Most importantly, using environmental data, the ML proved to be an effective method for predicting hypoxic conditions and harmful algal bloom events, an essential measurement in terms of environmental monitoring. Furthermore, machine learning was used to construct a number of databases for various species that will be useful to other researchers, and the creation of new algorithms will help the marine research community better comprehend the chemistry and biology of the ocean.

An in vivo coil setup for AC magnetic field-mediated magnetic nanoparticle heating experiments.

In vitro and in vivo evaluation of magnetic nanoparticles in relation to magnetic fluid hyperthermia (MFH) treatment is an on-going quest. This current paper demonstrates the design, fabrication, and evaluation of an in vivo coil setup for real-time, whole body thermal imaging. Numerical calculations estimating the flux densities, and in silico analysis suggest that the proposed in vivo coil setup could be used for real-time thermal imaging during MFH experiments (within the limitations due to issues of penetration depth). Such in silico evaluations provide insights into the design of suitable AMF applicators for AC magnetic field-mediated in vivo MNP heating as demonstrated in this study.